
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     Dict		,    _lowerCamelCase					:     Optional[Any])   ->       Tuple:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,    _lowerCamelCase):
														__UpperCamelCase      :  Any       =   np.full((len(_lowerCamelCase), sequence_length, 2)		,    _lowerCamelCase)
							else:
														__UpperCamelCase      :  Union[str, Any]       =   np.full((len(_lowerCamelCase), sequence_length)		,    _lowerCamelCase)

							for i, tensor in enumerate(_lowerCamelCase):
														if padding_side == "right":
																					if isinstance(_lowerCamelCase		,    _lowerCamelCase):
																												__UpperCamelCase      :  Tuple       =   tensor[:sequence_length]
																					else:
																												__UpperCamelCase      :  Union[str, Any]       =   tensor[:sequence_length]
														else:
																					if isinstance(_lowerCamelCase		,    _lowerCamelCase):
																												__UpperCamelCase      :  Tuple       =   tensor[:sequence_length]
																					else:
																												__UpperCamelCase      :  Dict       =   tensor[:sequence_length]

							return out_tensor.tolist()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   ord(_lowerCamelCase)
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
														return True
							__UpperCamelCase      :  str       =   unicodedata.category(_lowerCamelCase)
							if cat.startswith("P"):
														return True
							return False




@dataclass
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	42
	_A								=	True
	_A								=	None
	_A								=	None
	_A								=	-1_0_0
	_A								=	"pt"







	def   _lowerCamelCase							(   self						:int	,  a						:Optional[int]		)						->					List[str]:
								import torch

								__UpperCamelCase      :  List[Any]       =   "label" if "label" in features[0].keys() else "labels"
								__UpperCamelCase      :  int       =   [feature[label_name] for feature in features] if label_name in features[0].keys() else None
								__UpperCamelCase      :  Any       =   self.tokenizer.pad(
								    a	,  padding=self.padding	,  max_length=self.max_length	,  pad_to_multiple_of=self.pad_to_multiple_of	,  return_tensors="pt" if labels is None else None	,  )

								if labels is None:
															return batch

								__UpperCamelCase      :  str       =   torch.tensor(batch["entity_ids"]		).shape[1]
								__UpperCamelCase      :  str       =   self.tokenizer.padding_side
								if padding_side == "right":
															__UpperCamelCase      :  Optional[Any]       =   [
															    list(a		) + [self.label_pad_token_id] * (sequence_length - len(a		)) for label in labels
															]
								else:
															__UpperCamelCase      :  str       =   [
															    [self.label_pad_token_id] * (sequence_length - len(a		)) + list(a		) for label in labels
															]

								__UpperCamelCase      :  Tuple       =   [feature["ner_tags"] for feature in features]
								__UpperCamelCase      :  Optional[Any]       =   padding_tensor(a	,  -1	,  a	,  a		)
								__UpperCamelCase      :  Dict       =   [feature["original_entity_spans"] for feature in features]
								__UpperCamelCase      :  Any       =   padding_tensor(a	,  (-1, -1)	,  a	,  a		)
								__UpperCamelCase      :  Dict       =   {k: torch.tensor(a	,  dtype=torch.intaa		) for k, v in batch.items()}

								return batch

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)